Metaphor is often thought of as a decorative instrument of literature.
It is more scientifically productive to understand metaphor a basic
human cognitive capacity used to represent relationships between concepts.
Mathematics, for example, may be understood as an elaborate metaphorical
scaffolding where “embodied” concepts are everywhere, such as the fact
that multiplying by \(e^{i\theta }\)  rotates a point in the complex plane by \(\theta \) 
radians. Rotation is something we do every time we drive a car, and in many
other situations, so in
that way \(e^{i\theta }\)  is physically intuitive because we have been
rotating different physical objects our entire
lives [1]}, [2]}, [3]}.
In this paper we present a system to identify metaphorical violence (MV)
on cable news surrounding presidential electionsCode publically available: https://github.com/mt-digital/metacorps-nn. Just as our intuitions about
physical transformations
of physical objects help us reason about abstract mathematics, our intuitions
about violence serve as heuristics for rather more complex political events
(for a good discussion of the
importance of heuristics to cognition, see [4]}).
Specifically, we present a neural network classifier for
determining whether or not a phrase is MV or not. This effort is an
important part of a larger effort to improve the throughput of an
existing metaphor annotation software application we are developing
called Metacorpshttps://github.com/mt-digital/metacorps.
Knowing what metaphors are said and
when in a society is important because metaphors are representations of
a society's conceptual relationships. Large-scale observations of
metaphor use are currently limited because it is a time-intensive task
for humans to do. We demonstrate that even with a modest gold-standard
dataset, a neural network system can distinguish, to around 85%
accuracy, MV from non-MV. After introducing the motivation, we present
the data, our methods, and analyze the performance of some candidate
neural network classifiers. We close with a discussion of the promise
and challenges of integrating this system with teams of human
annotaters.
